Body-worn sensor for characterizing patients with heart failure

ABSTRACT

The invention provides a sensor for measuring both impedance and ECG waveforms that is configured to be worn around a patient&#39;s neck. The sensor features 1) an ECG system that includes an analog ECG circuit, in electrical contact with at least two ECG electrodes, that generates an analog ECG waveform; and 2) an impedance system that includes an analog impedance circuit, in electrical contact with at least two (and typically four) impedance electrodes, that generates an analog impedance waveform. Also included in the neck-worn system are a digital processing system featuring a microprocessor, and an analog-to-digital converter. During a measurement, the digital processing system receives and processes the analog ECG and impedance waveforms to measure physiological information from the patient. Finally, a cable that drapes around the patient&#39;s neck connects the ECG system, impedance system, and digital processing system.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/747,842, filed Dec. 31, 2012, which is hereby incorporated in itsentirety including all tables, figures, and claims.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to sensors for characterizing patientssuffering from congestive heart failure (CHF) and related diseases.

2. Description of the Related Art

CHF occurs when the heart is unable to sufficiently pump and distributeblood to meet the body's needs. CHF is typically preceded by an increaseof fluid in the thoracic cavity, and can by characterized by shortnessof breath, swelling of the legs and other appendages, and intolerance toexercise. It affects nearly 5.3M Americans and has an accompanying costof somewhere between $30-50B, with roughly $17B attributed to hospitalreadmissions. Such events are particularly expensive to hospitals, asreadmissions occurring within a 30-day period are not reimbursable byMedicare or private insurance as of October 2012.

In medical centers, CHF is typically detected using Doppler/ultrasound,which measures parameters such as stroke volume (SV), cardiac output(CO), and ejection fraction (EF). Gradual weight gain measured with asimple scale is one method to indicate CHF in the home environment.However, this parameter is typically not sensitive enough to detect theearly onset of CHF, a particularly important time when the condition maybe ameliorated by a change in medication or diet.

SV is the mathematical difference between left ventricular end-diastolicvolume (EDV) and end-systolic volume (ESV), and represents the volume ofblood ejected by the left ventricle with each heartbeat; a typical valueis about 80 mL. EF relates to EDV and ESV as described below in Eq. 1,with a typical value for healthy individuals being about 50-65%, and anejection fraction of less than 40% indicating systolic heart failure.

$\begin{matrix}{{EF} = {\frac{SV}{EDV} = \frac{{EDV} - {ESV}}{EDV}}} & (1)\end{matrix}$

CO is the average, time-dependent volume of blood ejected from the leftventricle into the aorta and, informally, indicates how efficiently apatient's heart pumps blood through their arterial tree; a typical valueis about 5 L/min. CO is the product of HR and SV, i.e.:

CO=SV×HR  (2)

CHF patients, in particular those suffering from systolic heart failure,may receive implanted devices, such as pacemakers and/or implantablecardioverter-defibrillators, to increase EF and subsequent blood flowthroughout the body. These devices also include technologies called‘OptiVol’ (from Medtronic) or ‘CorVue’ (St. Jude) that use circuitry andalgorithms within the implanted device to measure the electricalimpedance between different leads of the pacemaker. As thoracic fluidincreases in the CHF patient, the impedance typically is reduced. Thusthis parameter, when read by an interrogating device placed outside thepatient's body, can indicate the onset of heart failure.

Corventis Inc. has developed the AVIVO Mobile Patient Management (MPM)System to characterize ambulatory CHF patients. AVIVO is typically usedover a 7-day period, during which it provides continual insight into apatient's physiological status by steadily collecting data andwirelessly transmitting it through a small handheld device to a centralserver for analysis and review. The system consists of three parts: 1)The PiiX sensor, a patient-worn adhesive device that resembles a large(approximately 15″ long) bandage and measures fluid status,electrocardiography (ECG) waveforms, heart rate (HR), respiration rate,patient activity, and posture; 2) The zLink Mobile Transmitter, a small,handheld device that receives information from the Piix sensor and thentransmits data wirelessly to a remote server via cellular technology;and 3) the Corventis Monitoring Center, where data are collected andanalyzed. Technicians staff the Monitoring Center, review the incomingdata, and in response generate clinical reports made available toprescribing physicians by way of a web-based user interface.

In some cases, physicians can prescribe ECG monitors to ambulatory CHFpatients. These systems measure time-dependent waveforms, from whichheart rate HR and information related to arrhythmias and other cardiacproperties are extracted. They characterize ambulatory patients overshort periods (e.g. 24-48 hours) using ‘holier’ monitors, or over longerperiods (e.g. 1-3 weeks) using cardiac event monitors. Conventionalholter or event monitors typically include a collection of chest-wornECG electrodes (typically 3 or 5), an ECG circuit that collects analogsignals from the ECG electrodes and converts these into multi-lead ECGwaveforms; a processing unit then analyzes the ECG waveforms todetermine cardiac information. Typically the patient wears the entiresystem on their body. Some modern ECG-monitoring systems includewireless capabilities that transmit ECG waveforms and other numericaldata through a cellular interface to an Internet-based system, wherethey are further analyzed to generate, for example, reports describingthe patient's cardiac rhythm. In less sophisticated systems, theECG-monitoring system is worn by the patient, and then returned to acompany that downloads all relevant information into a computer, whichthen analyzes it to generate the report. The report, for example, may beimported into the patient's electronic medical record (EMR). The EMRavails the report to cardiologists or other clinicians, who then use itto help characterize the patient.

Measuring CO and SV in a continuous, non-invasive manner with highclinical accuracy has often been considered a ‘holy grail’ ofmedical-device monitoring. Most existing techniques in this fieldrequire in-dwelling catheters, which in turn can lead to complicationswith the patient, are inherently inaccurate in the critically ill, andrequire a specially trained operator. For example, current ‘goldstandards’ for this measurement are thermodilution cardiac output (TDCO)and the Fick Oxygen Principal (Fick). However both TDCO and Fick arehighly invasive techniques that can cause infection and othercomplications, even in carefully controlled hospital environments. InTDCO, a pulmonary artery catheter (PAC), also known as a Swan-Ganzcatheter, is typically inserted into the right portion of the patient'sheart. Procedurally a bolus (typically 10 ml) of glucose or saline thatis cooled to a known temperature is injected through the PAC. Atemperature-measuring device within the PAC, located a known distanceaway (typically 6-10 cm) from where fluid is injected, measures theprogressively increasing temperature of the diluted blood. CO is thenestimated from a measured time-temperature curve, called the‘thermodilution curve’. The larger the area under this curve, the lowerthe cardiac output Likewise, the smaller the area under the curveimplies a shorter transit time for the cold bolus to dissipate, hence ahigher CO.

Fick involves calculating oxygen consumed and disseminated throughoutthe patient's blood over a given time period. An algorithm associatedwith the technique incorporates consumption of oxygen as measured with aspirometer with the difference in oxygen content of centralized bloodmeasured from a PAC and oxygen content of peripheral arterial bloodmeasured from an in-dwelling cannula.

Both TD and Fick typically measure CO with accuracies between about0.5-1.0 l/min, or about +/−20% in the critically ill.

Several non-invasive techniques for measuring CO and SV have beendeveloped with the hope of curing the deficiencies of Fick and TD. Forexample, Doppler-based ultrasonic echo (Doppler/ultrasound) measuresblood velocity using the well-known Doppler shift, and has shownreasonable accuracy compared to more invasive methods. But both two andthree-dimensional versions of this technique require a specially trainedhuman operator, and are thus, with the exception of the esophagealDoppler technique, impractical for continuous measurements. CO and SVcan also be measured with techniques that rely on electrodes placed onthe patient's torso that inject and then collect a low-amperage,high-frequency modulated electrical current. These techniques, based onelectrical bioimpedance and called ‘impedance cardiography’ (ICG),‘electrical cardiometry velocimetry’ (ECV), and ‘bioreactance’ (BR),measure a time-dependent electrical waveform that is modulated by theflow of blood through the patient's thorax. Blood is a good electricalconductor, and when pumped by the heart can further modulate the currentinjected by these techniques in a manner sensitive to the patient's CO.During a measurement, ICG, ECV, and BR each extract properties calledleft ventricular ejection time (LVET) and pre-injection period (PEP)from time-dependent ICG and ECG waveforms. A processer then analyzes thewaveform with an empirical mathematical equation, shown below in Eq. 3,to estimate SV. CO is then determined from the product of SV and HR, asdescribed above in Eq. 2.

ICG, ECV, and BR all represent a continuous, non-invasive alternativefor measuring CO/SV, and in theory can be conducted with an inexpensivesystem and no specially trained operator. But the medical community hasnot embraced such methods, despite the fact that clinical studies haveshown them to be effective with some patient populations. In 1992, forexample, an analysis by Fuller et al. analyzed data from 75 publishedstudies describing the correlation between ICG and TD/Fick (Fuller etal., The validity of cardiac output measurement by thoracic impedance: ameta-analysis; Clinical Investigative Medicine; 15: 103-112 (1992)). Thestudy concluded using a meta analysis wherein, in 28 of these trials,ICG displayed a correlation of between r=0.80-0.83 against TDCO, dyedilution and Fick CO. Patients classified as critically ill, e.g. thosesuffering from acute myocardial infarction, sepsis, and excessive lungfluids, yielded worse results. Further impeding commercial acceptance ofthese techniques is the tendency of ICG monitors to be relatively bulkyand similar in both size and complexity to conventional vital signsmonitors. This means two large and expensive pieces of monitoringequipment may need to be located bedside in order to monitor a patient'svital signs and CO/SV. For this and other reasons, impedance-basedmeasurements of CO have not achieved widespread commercial success.

SUMMARY OF THE INVENTION

The current invention provides a simple, low-cost, non-invasive sensorthat measures CO, SV, fluid levels, ECG waveforms, HR, arrhythmias,temperature, location, and motion/posture/activity level from CHF andother patients. The sensor, which is shaped like a conventionalnecklace, is particularly designed for ambulatory patients: with thisform factor, it can be easily draped around a patient's neck, where itthen makes the above-described measurements during the patient'sday-to-day activities. Using a short-range wireless radio, the sensortransmits data to the patient's cellular telephone, which then processesand retransmits the data over cellular networks to a web-based system.The web-based system generates reports for supervising clinicians, whocan then adjust the patient's diet, exercise, and medication regime toprevent the onset of CHF.

The sensor features a miniaturized impedance-measuring system, describedin detail below, that is built into the necklace form factor. Thissystem measures a time-dependent, transbrachial impedance (TBI) waveformthat is then processed to determine CO, SV, and fluid levels, asdescribed in detail below. Accompanying this system is a collection ofalgorithms that perform signal processing and account for the patient'smotion, posture and activity level, as measured with an internalaccelerometer, to improve the calculations for all hemodynamicmeasurements. Compensation of motion is particularly important sincemeasurements are typically made from ambulatory patients. Also withinthe necklace is a medical-grade ECG system that measures single-lead ECGwaveform and accompanying values of HR and cardiac arrhythmias. Thesystem also analyzes other components of the ECG waveforms, whichinclude: i) a QRS complex; ii) a P-wave; iii) a T-wave; iv) a U-wave; v)a PR interval; vi) a QRS interval; vii) a QT interval; viii) a PRsegment; and ix) an ST segment. The temporal or amplitude-relatedfeatures of these components may vary over time, and thus thealgorithmic-based tools within the system, or software associated withthe algorithm-based tools, can analyze the time-dependent evolution ofeach of these components. In particular, algorithmic-based tools thatperform numerical fitting, mathematical modeling, or pattern recognitionmay be deployed to determine the components and their temporal andamplitude characteristics for any given heartbeat recorded by thesystem.

As an example, physiological waveforms measured with the sensor may benumerically ‘fit’ with complex mathematical functions, such asmulti-order polynomial functions or pre-determined, exemplary waveforms.These functions may then be analyzed to determine the specificcomponents, or changes in these components, within the waveform. Inrelated embodiments, waveforms may be analyzed with more complexmathematical models that attempt to associate features of the waveformswith specific bioelectric events associated with the patient.

Each of the above-mentioned components corresponds to a differentfeature of the patient's cardiac system, and thus analysis of themaccording to the invention may determine or predict the onset of CHF.

Other conditions that can be determined through analysis of ECGwaveforms include: blockage of arteries feeding the heart (each relatedto the PR interval); aberrant ventricular activity or cardiac rhythmswith a ventricular focus (each related to the QRS interval); prolongedtime to cardiac repolarization and the onset of ventricular dysrhythmias(each related to the QT interval); P-mitrale and P-pulmonale (eachrelated to the P-wave); hyperkalemia, myorcardial injury, myocardialischemia, myocardial infarction, pericarditis, ventricular enlargement,bundle branch block, and subarachnoid hemorrhage (each related to theT-wave); and bradycardia, hypokalemia, cardiomyopathy, and enlargementof the left ventricle (each related to the U-wave). These are only asmall subset of the cardiac conditions that may be determined orestimated through analysis of the ECG waveform according to theinvention.

In one aspect, the invention provides a sensor for measuring bothimpedance and ECG waveforms that is configured to be worn around apatient's neck. The sensor includes: 1) an ECG system featuring ananalog ECG circuit, in electrical contact with at least two ECGelectrodes, that generates an analog ECG waveform; and 2) an impedancesystem featuring an analog impedance circuit, in electrical contact withat least two (and typically four) impedance electrodes, that generatesan analog impedance waveform. Also included in the neck-worn system area digital processing system featuring a microprocessor, and ananalog-to-digital converter. During a measurement, the digitalprocessing system receives and processes the analog ECG and impedancewaveforms to measure physiological information from the patient.Finally, a cable that drapes around the patient's neck electrically andmechanically connects the ECG system, impedance system, and digitalprocessing system.

In embodiments, the cable features a plurality of conducting wires thatconnect the ECG and impedance systems to the digital processing system.For example, the sensor may include a flexible circuit made from atape-like material such as Kapton. In embodiments, the system featuresat least two non-flexible circuit boards, connected to each other withthe flexible circuit, to form a ‘sensor necklace’. Typically thenecklace includes multiple, alternative flexible and non-flexiblesystems. Circuitry for the ECG, impedance, and digital processingsystems is typically located on the non-flexible circuit boards.

In other embodiments, the cable includes a first ECG electrode in afirst segment of the necklace that contacts a first side of thepatient's chest, and a second ECG electrode in a second segment thatcontacts a second, opposing side of the patient's chest. For theimpedance measurement, the cable also includes first and secondimpedance electrodes in, respectively, the first and second segments ofthe necklace. These electrodes are opposing sides of the patient's chestto make the impedance measurements.

In preferred embodiments, the impedance system features four distinctelectrodes, i.e. a first current-injecting electrode, a secondcurrent-injecting electrode, a first voltage-measuring electrode, and asecond voltage-measuring electrode. Here, the cable features a firstsegment that includes a first ECG electrode, the first current-injectingelectrode, and the first voltage-measuring electrode, and a secondsegment that includes a second ECG electrode, the secondcurrent-injecting electrode, and the second voltage-measuring electrode.As before, the first and second segments are configured to contactopposing sides of the patient's chest.

A battery system powers the ECG, the impedance, and the digitalprocessing systems. To complement the necklace design, the cable used toconnect these systems also includes the battery system. Morespecifically, the cable includes a first connector and the batterysystem includes a second connector, with the first connector mated tothe second connector so that the battery system can be detachableremoved. The cable can also include a wireless transceiver based on aprotocol such as Bluetooth and/or 802.11-based transceiver, as well as aUSB connector in electrical contact with a flash memory system.

In another aspect, the invention provides a method for monitoring anelectrical impedance from a patient. The method comprising the followingsteps: 1) providing a loop-shaped, flexible member, configured to bepositioned around the patient's neck, that includes: i) at least fourelectrodes, each connected to the flexible member, where a first set ofelectrodes injects electrical current into the patient near their neck,and a second set of electrodes measures electrical signals from thepatient; ii) an impedance-measuring system within the flexible memberand in electrical contact with the second set of electrodes; and iii) adata-processing system, also within the flexible member and inelectrical contact with the impedance-measuring system; 2) injectingelectrical current into the patient near their neck with at least oneelectrode in the first set of electrodes; 3) measuring a voltage withthe second set of electrodes, where the voltage relates to a product ofthe injected current and an impedance of the patient; and 4) processingthe voltage to determine an impedance value.

In embodiments, the method includes step of measuring a voltage with thesecond set of electrodes using a differential amplifier configured tomeasure a time-dependent voltage indicating the product of electricalimpedance near the patient's chest and current injected by the secondset of electrodes. The time-dependent voltage can indicate how fluidlevels and respiration affect electrical impedance in the patient'schest, and can thus be used to estimate these parameters. In otherembodiments, the differential amplifier generates a time-dependentvoltage that indicates how heartbeat-induced blood flow affectselectrical impedance in the patient's chest. Here, the method includesprocessing the time-dependent voltage with a computer algorithm toestimate the patient's SV, CO, and/or HR, with the equations central tothese algorithms described below in Eqs. 1-4. The method can alsoinclude the step of measuring an ECG waveform with an ECG system, theECG system being embedded within the loop-shaped, flexible member. Here,the method processes the ECG waveform to determine HR, arrhythmias, HRvariability, and other cardiac properties. In all cases, the methodincludes the step of wirelessly transmitting information to an externalcomputer, such as a central monitoring station in a hospital, or acellular telephone.

In another aspect, the invention provides a method for generating analarm indicating fluid build-up for a patient using the sensor andmethods described herein. Here, the method uses a computer algorithm toestimate the fluid levels in the patient's chest, and then comparestrends in these values, or related values such as impedance or voltagemeasured with the sensor, to one or more pre-determined values. Themethod generates an alarm when one or more impedance values in the trendin impedance values, or a slope in these values, exceeds thepre-determined value. In related embodiments, the alarm is onlygenerated when the parameter of interest exceeds the pre-determinedvalue for a pre-determined period of time. When the alarm is generated,the method transmits it to the central monitoring station, cellulartelephone, or other device. In general, alarms can be generated usingany parameter measured by the sensor described herein, e.g. SV, CO, HR,or motion/posture/activity level.

The invention has many advantages. In general, it combines a comfortablesensor system with a web-based software system that, working in concert,allow a clinician to monitor a robust set of cardiovascular parametersfrom a CHF patient. The cardiovascular parameters feature thoseassociated with the heart's mechanical properties (i.e. CO and SV) andelectrical properties (i.e. HR and ECG). Taken collectively, these givethe clinician a unique insight into the patient's condition.

These and other advantages will be apparent from the following detaileddescription, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a three-dimensional image of a necklace-shaped sensor thatmeasures CO, SV, fluid levels, ECG waveforms, HR, arrhythmias, andmotion/posture/activity level from an ambulatory patient;

FIG. 2 shows two and three-dimensional images of the sensor of FIG. 1worn around a patient's neck;

FIG. 3 shows a three-dimensional image of an embodiment of thenecklace-shaped sensor that features alternating flexible andnon-flexible circuit boards integrated directly into a cable configuredto drape around the patient's neck;

FIG. 4 shows photographs of systems described in the prior art that useimpedance measurements to monitor a patient;

FIG. 5 shows a schematic drawing of electrodes used for the ECG andimpedance systems positioned on the patient's chest using the sensor ofFIGS. 1 and 3;

FIG. 6 shows a mechanical drawing of the sensor of FIG. 1 and itsassociated electrodes for ECG and impedance measurements;

FIG. 7 shows a mechanical drawing of the sensor of FIG. 1 and itsassociated electronics for ECG, impedance, and digital processingsystems;

FIG. 8 shows a mechanical drawing of the sensor of FIG. 1 and itsassociated battery and data-transfer systems;

FIG. 9 shows a schematic drawing of the sensor of FIG. 1 transmittingdata to a computer server using the patient's cellular telephone and/orpersonal computer;

FIG. 10 shows screen captures from a software application operating onthe cellular telephone of FIG. 9;

FIG. 11 shows a schematic drawing of an electrical circuit used withinthe sensor of FIG. 1 to make the impedance measurement;

FIG. 12 shows a flow chart of an algorithm used to calculate SV duringperiods of motion;

FIG. 13 shows graphs of time-dependent impedance waveforms measured witha prototype of the sensor of FIG. 1;

FIG. 14 shows graphs of time-dependent ECG waveforms, measured with theprototype of the sensor of FIG. 1, from two different locations on thepatient's thoracic cavity;

FIG. 15 shows a mathematical derivative of a time-dependent TBIwaveform;

FIG. 16 shows Bland-Altman (left) and correlation (right) graphs of SVmeasured with a technique similar to TBI and magnetic resonance imaging(MRI) during a clinical trial;

FIG. 17 shows correlation graphs of data, averaged from 39 subjects, ofbaseline impedance (top) and SV (bottom) compared to lower body negativepressure (LBNP) level, which is an experimental technique for simulatingCHF;

FIG. 18 shows graphs of Pearson's correlation coefficients (r), measuredfrom each of the subjects used to generate the data for FIG. 17,describing the relationship of baseline impedance and SV to LBNP; and

FIG. 19 shows a schematic drawing of a system that includes the sensorof FIG. 1 and a web-based software system used to monitor anelectrophysiology (EP) procedure.

DETAILED DESCRIPTION OF THE INVENTION

As shown in FIGS. 1 and 2, the invention provides a physiological sensor30 that, during use, is comfortably worn around the patient's neck likea conventional necklace. The sensor 30 is designed for patientssuffering from CHF and other cardiac diseases, such as cardiacarrhythmias, as well as patients with implanted devices such aspacemakers and ICDs. It makes impedance measurements to determine CO,SV, and fluid levels, and ECG measurements to determine a time-dependentECG waveform and HR. Additionally it measures respiratory rate, skintemperature, location, and motion-related properties such as posture,activity level, falls, and degree of motion. The sensor's form factor isdesigned for both one-time measurements, which take just a few minutes,and continuous measurements, which can take several days. Necklaces arelikely familiar to a patient 10 wearing the sensor 30, and this in turnmay improve their compliance in making measurements as directed by theirphysician. Ultimately compliance in using the sensor may improve thepatient's physiological condition. Moreover, the sensor is designed tomake measurements near the center of the chest, which is relativelyinsensitive to motion compared to distal extremities, like the arms orhands. The sensor's form factor also ensures relatively consistentelectrode placement for the impedance and ECG measurements; this isimportant for one-time measurements made on a daily basis, as itminimizes day-to-day errors associated with electrode placement.Finally, the sensor's form factor distributes electronics around thepatient's neck, thereby minimizing bulk and clutter associated withthese components and making the sensor 30 more comfortable to thepatient.

In one embodiment the sensor 30 features a pair of electrode holders34A, 34B, located on opposing sides of the necklace, that each receive aseparate 3-part electrode patch 35, 37, shown in more detail in FIG. 6.During use, the electrode patches 35, 37 snap into their respectiveelectrode holders 34A, 34B, and then stick to the patient's chest whenthe sensor 30 is draped around their neck. An adhesive backing 33, 39supports each conductive electrode 31A-C, 41A-C within the electrodepatch 35, 37. The electrodes 31A-C, 41A-C feature a sticky, conductivegel that contacts the patient's skin. The conductive gel contacts ametal rivet that is coated on one side with a thin layer of Ag/AgCl, andis designed to snap into a mated connector within the electrode holders34A, 34B. As shown in more detail in FIG. 5, the outer electrodes 31A,31C, 41A, 41C in each electrode patch are used for the impedancemeasurement (they conduct signals V+/−, I+/−), while the innerelectrodes 31B, 41B are used for the ECG measurement (they conductsignals ECG+/−). Proper spacing of the electrodes 31A, 31C, 41A, 41Censures both impedance and ECG waveforms having high signal-to-noiseratios; this in turn leads to measurements that are relatively easy toanalyze, and thus have optimum accuracy. FIG. 6 shows preferreddimensions for these components.

A flexible, flat cable 38 featuring a collection of conductive memberstransmits signals from the electrode patches 35, 37 to an electronicsmodule 36, which, during use, is preferably worn near the back of theneck. The electronic module 36 may snap into a soft covering to increasecomfort. The electronics module 36, as described in detail below withreference to FIG. 7, features a first electrical circuit 61 for makingan impedance-based measurement of TBI waveforms that yield CO, SV, andfluid levels, and a second electrical circuit 64 for making differentialvoltage measurements of ECG waveforms that yield HR and arrhythmiainformation. The first electrical circuit 61, which is relativelycomplex, is shown schematically in FIG. 11; the second electricalcircuit 64 is well known in this particular art, and is thus notdescribed in detail here.

During a measurement, the second electrical circuit 64 measures ananalog ECG waveform that is received by an internal analog-to-digitalconverter within a microprocessor 62. The microprocessor analyzes thissignal to simply determine that the electrode patches are properlyadhered to the patient, and that the system is operating satisfactorily.Once this state is achieved, the first 61 and second 64 electricalcircuits generate time-dependent analog waveforms that a high-resolutionanalog-to-digital converter 62 within the electronics module 36 receivesand then sequentially digitizes to generate time-dependent digitalwaveforms. Analog waveforms can be switched over to this component, forexample, using a field effect transistor (FET). Typically thesewaveforms are digitized with 16-bit resolution over a range of about −5Vto 5V. The microprocessor 62 receives the digital waveforms andprocesses them with computational algorithms, written in embeddedcomputer code (such as C or Java), to generate values of CO, SV, fluidlevel, and HR. An example of an algorithm is described with reference toFIG. 15. Additionally, the electronics module 36 features a 3-axisaccelerometer 65 and temperature sensor 67 to measure, respectively,three time-dependent motion waveforms (along x, y, and z-axes) andtemperature values. The microprocessor 62 analyzes the time-dependentmotion waveforms to determine motion-related properties such as posture,activity level, falls, and degree of motion. Temperature values indicatethe patient's skin temperature, and can be used to estimate their coretemperature (a parameter familiar to physicians), as well as ancillaryconditions, such as perfusion, ambient temperature, and skin impedance.Motion-related parameters are determined using techniques known in theart, and are described in more detail with reference to FIG. 12.Temperature values are preferably reported in digital form that themicroprocessor receives through a standard serial interface, such asI2C, SPI, or UART.

Both numerical and waveform data processed with the microprocessor 62are ported to a wireless transmitter 66 within the electronics module36, such as a transmitter based on protocols like Bluetooth or802.11a/b/g/n. From there, the transmitter 66 sends data to an externalreceiver, such as a conventional cellular telephone 20, tablet, wirelesshub (such as Qualcomm's 2Net system), or personal computer. Devices likethese can serve as a ‘hub’ to forward data to an Internet-connectedremote server located, e.g., in a hospital, medical clinic, nursingfacility, or eldercare facility, as shown in FIG. 9.

Referring back to FIG. 1, and in more detail in FIG. 8, a battery module32 featuring a rechargeable Li:ion battery 48 connects at two points tothe cable 38 using a pair of connectors 29A, 29B. During use, theconnectors 29A, 29B plug into a pair of mated connectors 44A, 44B thatsecurely hold the terminal ends of the cable 38 so that the sensor 30can be comfortably and securely draped around the patient's neck.Importantly, when both connectors 29A, 29B are plugged into the batterymodule 32, the circuit within the sensor 30 is completed, and thebattery module 32 supplies power to the electronics module 36 to drivethe above-mentioned measurements. The connectors 29A, 29B terminatingthe cable can also be disconnected from the connectors 44A, 44B on thebattery module 32 so that this component can be replaced withoutremoving the sensor 30 from the patient's neck. Replacing the batterymodule 32 in this manner means the sensor 30 can be worn for extendedperiods of time without having to remove it from the patient. Ingeneral, the connectors 29A, 29B can take a variety of forms: they canbe flat, multi-pin connectors, such as those shown in FIG. 1, orstereo-jack type connectors, such as those shown in FIG. 8, that quicklyplug into a female adaptor. Both sets of connectors 29A, 29B, 44A, 44Bmay also include a magnetic coupling so that they easily snap together,thereby making the sensor easy to apply. Typically an LED 27 on thebattery module indicates that this is the case, and that the system isoperational. When the battery within battery module 32 is nearlydrained, the LED 27 indicates this particular state (e.g., by changingcolor, or blinking periodically). This prompts a user to unplug thebattery module 32 from the two connectors, plug it into a rechargecircuit (not shown in the figure), and replace it with a fresh batterymodule as described above. Also contained within the battery module is aflash memory card 23 for storing numerical and waveform data, and amicro-USB port 25 that connects to the flash memory card 23 fortransferring data to a remote computer 24. Typically the micro-USB port25 is also used for recharging the battery when the sensor is removedfrom the patient. In embodiments, these components can also be moved tothe electronics module 36.

As is clear from FIG. 1, the neck-worn cable 38 serves four distinctpurposes: 1) it transfers power from the battery module 32 to theelectronics module 36; 2) it ports signals from the electrode patches35, 37 to the impedance and ECG circuits; 3) it ensures consistentelectrode placement for the impedance and ECG measurements to reducemeasurement errors; and 4) it distributes the various electronicscomponents and thus allows the sensor to be comfortably worn around thepatient's neck. Typically each arm of the cable 38 will have 6 wires: 2for the impedance electrodes 41A, 41C, 1 for the ECG electrode, and 3 topass signals from the electronics module to electrical components withinthe battery module (flash memory card 23, LED 27). These wires can beincluded as discrete elements, a flex circuit, or, as described above, aflexible cable.

FIG. 2 shows the above-described sensor 30 worn around the neck of apatient 10. As described above, the sensor 30 includes an electronicsmodule 36 worn on the back of the patient's neck, a battery module 32 inthe front, and electrode holders 34A, 34B that connect to a cable 38draped around the neck that make impedance and ECG measurements.

FIG. 3 shows an alternate sensor 30A, also featuring a necklace formfactor described above, only all circuit elements used for the TBI andECG measurements, along with those for digital signal processing andwireless data transmission, are integrated directly into the cable 38Athat wraps around the patient's neck. In this design, the sensor's cableincludes all circuit elements, which are typically distributed on analternating combination of rigid, fiberglass circuit boards and flexibleKapton circuit boards. Typically these circuit boards are potted with aprotective material, such as silicone rubber, to increase patientcomfort and protect the underlying electronics. The battery for thisdesign can be integrated directly into the cable, or connect to thecable with a conventional connector, such as a stereo-jack connector,micro-USB connector, or magnetic interface.

Referring again to FIG. 3, the necklace sensor 30A features alternatingsegments of multi-layer fiberglass-based circuit boards 75A-D andsingle-layer flexible, Kapton tape-based conducting elements 77A-F.Typically the Kapton tape-based conducting elements 77A-F are sandwichedbetween layers of the fiberglass-based circuit boards to ensure thatthey don't easily detach. To electrically connect the appropriateelements in the circuit boards 75A-D and conducting elements 77A-F, aclear hole can be drilled in the circuit board 75A-D and then filledwith conductive solder. Typically the length of segments of the circuitboards 75A-D and conductive elements 77A-F is no more than a fewcentimeters; this ensures that the sensor 30A comfortably drapes aroundthe patient's neck like a conventional necklace. Also dispersed alongthe span of the cable 38A are a pair of 3-snap electrode holders 34A,34B that receive corresponding 3-part conductive electrode patches 35,37; an electronics module 36 positioned near the center of the cable 38Aso that, during use, it is positioned near the back of the patient'sneck; and a wireless transmission module 66 similar to that describedabove. The cable 38A is terminated with a pair of magnetically activeleads 79A, 79B (+/−) that are attracted to opposing, magnetically activepoles of a battery module 32. During use, when the sensor 30A is drapedaround the patient's neck, the battery module 32 is drawn to themagnetically active leads 79A, 79B and automatically snaps into place.An electrical connection is established that provides power to all theelectrical elements described above. The battery module 32 is simplysnapped off of the magnetically active leads 79A, 79B and replaced whenit is running low on power. Such a design is meant to optimize batteryreplacement for patients with compromised dexterity, e.g. elderlypatients with CHF.

FIG. 9 depicts how the sensor 30 shown in FIG. 1 is designed tofacilitate remote monitoring of a patient 10. As shown in the topportion of the figure, after the sensor 30 measures the patient, itautomatically transmits data through its internal Bluetooth wirelesstransmitter to the patient's cellular telephone 20. In this case, thecellular telephone 20 preferably runs a downloadable softwareapplication that accesses the phone's internal Bluetooth drivers, andincludes a simple patient-oriented application that renders data on thephone's screen. From there, using its internal modem, the cellulartelephone 20 transmits data to an IP address associated with a computerserver 22. The computer server 22, in turn, renders a web-based systemthat displays data for clinicians at a hospital, medical clinic, nursingfacility, or eldercare facility. The web-based system may show ECG andTBI waveforms, trended numerical data, the patient's medical history,along with their demographic information. A clinician viewing theweb-based system may, for example, analyze the data and then call thepatient 10 and have them adjust their medications or diet.Alternatively, as shown in the lower half of the figure, the sensor 30can automatically transmit data through Bluetooth to a personal computer24, which then uses a wired or wireless Internet connection to transmitdata to the computer server 22. Here, the personal computer 24 runs acustom software program to download data from the sensor 22, display itfor the patient in an easy-to-understand format, and then forward it tothe computer server for a relatively complex analysis as describedabove. In yet another embodiment, the sensor 30 is directly plugged intothe personal computer 24 through a USB connection, and data aredownloaded using a wired connection and forwarded to the computer server22 as described above.

FIG. 10 shows examples of user interfaces 90, 91, 92 that integrate withthe above-mentioned systems and run on the cellular telephone 20, shownin this case as an iPhone. The user interfaces show information such aspatient demographics (interface 90), patient-oriented messages(interface 91), and numerical vital signs and time-dependent waveforms(interface 92). The interfaces shown in the figures are designed for thepatient. More screens, of course, can be added, and similar interfaces(preferably with more technical detail) can be designed for the actualclinician. The interfaces can also be used to render operationalreports, such as those generated with the system of FIG. 19. Reportsshowing similar data are, of course, possible.

FIG. 4 shows competing systems in the prior art that make impedancemeasurements from a patient. For example, the system 5 on the left istypically wheeled on a cart, and connects to electrodes worn on thepatient's body through a collection of wired leads. It typicallymeasures CO, SV, ECG, and HR in a medical clinic or hospital. The system6 shown in the middle features an electronics box that can be carried bythe patient or attached to their clothing, and, like the system 5 shownon the left, connects to the patient with a collection of wired leads tomeasure CO and SV. It is typically used for ambulatory patients. And thesystem 7 shown on the right is a single patch worn on the patient'schest that measures fluids in the thoracic cavity. This too is typicallyused for ambulatory patients.

FIG. 5 indicates in more detail how the above-described sensor measuresTBI waveforms and CO/SV values from a patient. As described above,3-part electrode patches 35, 37 within the neck-worn sensor attach tothe patient's chest. Ideally, each patch 35, 37 attaches just below thecollarbone near the patient's left and right arms. During a measurement,the impedance circuit injects a high-frequency, low-amperage current (I)through outer electrodes 31C, 41C. Typically the modulation frequency isabout 70 kHz, and the current is about 4 mA. The current injected byeach electrode 31C, 41C is out of phase by 180°. It encounters static(i.e. time-independent) resistance from components such as bone, skin,and other tissue in the patient's chest. Additionally, blood conductsthe current to some extent, and thus blood ejected from the leftventricle of the heart into the aorta offers a dynamic (i.e.time-dependent) resistance. The aorta is the largest artery passingblood out of the heart, and thus it has a dominant impact on the dynamicresistance; other vessels, such as the superior vena cava, willcontribute in a minimal way to the dynamic resistance.

Inner electrodes 31A, 41A measure a time-dependent voltage (V) thatvaries with resistance (R) encountered by the injected current (I). Thisrelationship is based on Ohm's Law (V=I×R). During a measurement, thetime-dependent voltage is filtered by the impedance circuit, andultimately measured with an analog-to-digital converter within theelectronics module. This voltage is then processed to calculate SV withan equation such as that shown below in Eq. 3, which is Sramek-Bernsteinequation, or a mathematical variation thereof. Historically parametersextracted from TBI signals are fed into the equation, shown below, whichis based on a volumetric expansion model taken from the aortic artery:

$\begin{matrix}{{SV} = {\delta \frac{L^{3}}{4.25}\frac{\left( {{{Z(t)}}/{t}} \right)_{\max}}{Z_{0}}{LVET}}} & (3)\end{matrix}$

In Eq. 3, Z(t) represents the TBI waveform, δ represents compensationfor body mass index, Zo is the base impedance, L is estimated from thedistance separating the current-injecting and voltage-measuringelectrodes on the thorax, and LVET is the left ventricular ejectiontime, which can be determined from the TBI waveform, or from the HRusing an equation called ‘Weissler's Regression’, shown below in Eq. 4,that estimates LVET from HR:

LVET=−0.0017×HR+0.413  (4)

Weissler's Regression allows LVET, to be estimated from HR determinedfrom the ECG waveform. This equation and several mathematicalderivatives, along with the parameters shown in Eq. 3, are described indetail in the following reference, the contents of which areincorporated herein by reference: Bernstein, Impedance cardiography:Pulsatile blood flow and the biophysical and electrodynamic basis forthe stroke volume equations; J Electr Bioimp; 1: 2-17 (2010). Both theSramek-Bernstein Equation and an earlier derivative of this, called theKubicek Equation, feature a ‘static component’, Z₀, and a ‘dynamiccomponent’, ΔZ(t), which relates to LVET and a (dZ/dt)_(max)/Z₀ value,calculated from the derivative of the raw TBI signal, ΔZ(t). Theseequations assume that (dZ(t)/dt)_(max)/Z₀ represents a radial velocity(with units of Ω/s) of blood due to volume expansion of the aorta.

FIG. 11 shows an analog circuit 100 that performs the impedancemeasurement according to the invention. The figure shows just oneembodiment of the circuit 100; similar electrical results can beachieved using a design and collection of electrical components thatdiffer from those shown in the figure.

The circuit 100 features a first electrode 115A that injects ahigh-frequency, low-amperage current (I₁) into the patient's brachium.This serves as the current source. Typically a current pump 102 providesthe modulated current, with the modulation frequency typically beingbetween 50-100 KHz, and the current magnitude being between 0.1 and 10mA. Preferably the current pump 102 supplies current with a magnitude of4 mA that is modulated at 70 kHz through the first electrode 115A. Asecond electrode 117A injects an identical current (I₂) that is out ofphase from I₁ by 180°.

A pair of electrodes 115B, 117B measure the time-dependent voltageencountered by the propagating current. These electrodes are indicatedin the figure as V+ and V−. As described above, using Ohm's law (V=I×R),the measured voltage divided by the magnitude of the injected currentyields a time-dependent resistance to ac (i.e. impedance) that relatesto blood flow in the aortic artery. As shown by the waveform 128 in thefigure, the time-dependent resistance features a slowly varying dcoffset, characterized by Zo, that indicates the baseline impedanceencountered by the injected current; for TBI this will depend, forexample, on the amount of fat, bone, muscle, and blood volume in thechest of a given patient. Zo, which typically has a value between about10 and 150Ω, is also influenced by low-frequency, time-dependentprocesses such as respiration. Such processes affect the inherentcapacitance near the chest region that TBI measures, and are manifestedin the waveform by low-frequency undulations, such as those shown in thewaveform 128. A relatively small (typically 0.1-0.5Ω) AC component,ΔZ(t), lies on top of Zo and is attributed to changes in resistancecaused by the heartbeat-induced blood that propagates in the brachialartery, as described in detail above. ΔZ(t) is processed with ahigh-pass filter to form a TBI signal that features a collection ofindividual pulses 130 that are ultimately processed to ultimatelydetermine SV and CO.

Voltage signals measured by the first electrode 115B (V+) and the secondelectrode 117B (V−) feed into a differential amplifier 107 to form asingle, differential voltage signal which is modulated according to themodulation frequency (e.g. 70 kHz) of the current pump 102. From there,the signal flows to a demodulator 106, which also receives a carrierfrequency from the current pump 102 to selectively extract signalcomponents that only correspond to the TBI measurement. The collectivefunction of the differential amplifier 107 and demodulator 106 can beaccomplished with many different circuits aimed at extracting weaksignals, like the TBI signal, from noise. For example, these componentscan be combined to form a ‘lock-in amplifier’ that selectively amplifiessignal components occurring at a well-defined carrier frequency. Or thesignal and carrier frequencies can be deconvoluted in much the same wayas that used in conventional AM radio using a circuit that features oneor more diodes. The phase of the demodulated signal may also be adjustedwith a phase-adjusting component 108 during the amplification process.In one embodiment, the ADS 1298 family of chipsets marketed by TexasInstruments may be used for this application. This chipset featuresfully integrated analog front ends for both ECG and impedancepneumography. The latter measurement is performed with components fordigital differential amplification, demodulation, and phase adjustment,such as those used for the TBI measurement, that are integrated directlyinto the chipset.

Once the TBI signal is extracted, it flows to a series of analog filters110, 112, 114 within the circuit 100 that remove extraneous noise fromthe Zo and ΔZ(t) signals. The first low-pass filter 110 (30 Hz) removesany high-frequency noise components (e.g. power line components at 60Hz) that may corrupt the signal. Part of this signal that passes throughthis filter 110, which represents Zo, is ported directly to a channel inan analog-to-digital converter 120. The remaining part of the signalfeeds into a high-pass filter 112 (0.1 Hz) that passes high-frequencysignal components responsible for the shape of individual TBI pulses130. This signal then passes through a final low-pass filter 114 (10 Hz)to further remove any high-frequency noise. Finally, the filtered signalpasses through a programmable gain amplifier (PGA) 116, which, using a1.65V reference, amplifies the resultant signal with acomputer-controlled gain. The amplified signal represents ΔZ(t), and isported to a separate channel of the analog-to-digital converter 120,where it is digitized alongside of Zo. The analog-to-digital converterand PGA are integrated directly into the ADS1298 chipset describedabove. The chipset can simultaneously digitize waveforms such as Zo andΔZ(t) with 24-bit resolution and sampling rates (e.g. 500 Hz) that aresuitable for physiological waveforms. Thus, in theory, this one chipsetcan perform the function of the differential amplifier 107, demodulator108, PGA 116, and analog-to-digital converter 120. Reliance of just asingle chipset to perform these multiple functions ultimately reducesboth size and power consumption of the TBI circuit 100.

Digitized Zo and ΔZ(t) waveforms are received by a microprocessor 124through a conventional digital interface, such as a SPI or I2Cinterface. Algorithms for converting the waveforms into actualmeasurements of SV and CO are performed by the microprocessor 124. Themicroprocessor 124 also receives digital motion-related waveforms froman on-board accelerometer, and processes these to determine parameterssuch as the degree/magnitude of motion, frequency of motion, posture,and activity level.

FIG. 12 shows a flow chart of an algorithm 133A that functions usingcompiled computer code that operates, e.g., on the microprocessor 124shown in FIG. 6. The algorithm 133A is used to measure TBI waveforms inthe presence of motion. The compiled computer code is loaded in memoryassociated with the microprocessor, and is run each time a TBImeasurement is converted into a numerical value for CO and SV. Themicroprocessor typically runs an embedded real-time operating system.The compiled computer code is typically written in a language such as C,C++, Java, or assembly language. Each step 135-150 in the algorithm 133Ais typically carried out by a function or calculation included in thecompiled computer code.

Physiological data similar to that generated with the sensor describedabove is shown in FIGS. 13-19. As shown in the top portion of FIG. 13,the TBI waveform is a time-dependent signal, with different componentsof the signal corresponding to unique physiological events. For example,the baseline of the TBI waveform corresponds to the relative fluid levelin the patient's thoracic cavity. This parameter typically increases forpatients entering into heart failure. Data shown in the figurecorrespond to a patient that was initially in an upright position. Otherthan the rapid time-dependent oscillations, which are described in moredetail below, the average baseline is relatively constant. The patientis then rapidly inverted, resulting in the drop in baseline shown atpoint 201. In this case, conductive bodily fluid pools in the patient'sthoracic cavity, thus increasing the conductivity of current injectedduring the impedance measurement, and consequently decreasing theresistance (i.e. impedance) measured near the chest. A short time later,the patient is reverted to their original, standing-up position. Fluiddrains quickly from their thoracic cavity as indicated by point 203,thus reducing conductivity and increasing impedance. FIG. 12 indicatesthat these small changes in thoracic fluid result in clear, measurablechanges in impedance, as shown in the TBI waveform. For patients withCHF, the change in fluid level will be more gradual, likely happeningover several days. Making one-time measurements with the sensor overthis period can monitor such changes. A gradual increase in fluidlevels, monitored with the end-to-end system shown in FIG. 8, willgradually decrease thoracic impedance levels measured with the sensordescribed herein. Automated computer algorithms, or technicians workingin a call center and trained to interpret data, can alert a supervisingclinician to the patient's status. The clinician can respond to thepatient's increase in fluid levels by ordering a change in diet (e.g. toreduce sodium content), exercise, or by altering the medication (e.g. byincreasing a dose of a diuretic, such as Lasix).

The bottom portion of FIG. 13 shows how respiratory rate, CO, and SV canbe extracted from the TBI waveform. The low-frequency oscillations inthe waveforms, as shown by points 204, indicate breathing-inducedchanges in thoracic impedance. Thus they can be analyzed (e.g. countedwith a simple beatpicking algorithm) to accurately determine thepatient's respiratory rate. Such a determination is important during theonset of heart failure, as an increase in respiration rate oftenindicates a gradual lowering of oxygen-containing blood being pumped bythe patient's heart. These data, combined with data describing thoracicfluid levels, can be used to identify a patient entering into heartfailure. The relatively high-frequency oscillations in the plot, shownby point 202, indicate cardiac pulses. Here, blood pumped by the heartinto the aorta, which because of its hemoglobin is a good electricalconductor, results in a rapid, time-dependent change in the patient'sthoracic impedance. This change is indicated by a collection ofheartbeat-induced pulses that are analyzed as described below todetermine SV. From there, SV and CO are calculated using Eqs. 3 and 4,as described above.

Somewhat surprisingly, as shown in FIG. 14, ECG waveforms can beaccurately measured near the neck with the sensor described herein. Thetop portion of the figure shows an ECG waveform measured with aconventional Lead II placement 253, as indicated by the torso 250 in thefigure. Here, a first electrode is placed on the right-hand side of thetorso 250, about 5 cm below the collarbone, and a second electrode onthe left-hand side of the torso 250, about 15 cm above the waist. Incontrast, the bottom portion of the figure shows the lead placementaccording to the necklace-shaped sensor. A torso 251 in the figureindicates the placement of electrodes 255 for this measurement.Comparing the two waveforms indicates a nearly identical morphology forthis particular patient. The waveform measured by the necklace sensorhas a relatively low signal-to-noise ratio, likely because the sensingelectrodes are relatively close together, thus resulting in a weakersignal. But any high-frequency noise in this case can be easily removedusing a simple digital low-pass filter (cutoff around 40 Hz) or evensimple techniques based on, e.g., a running average. HR and associatedcardiac arrhythmias can then be easily determined through analysis ofthe well-known QRS complexes in the figure.

FIG. 15 indicates how LVET is extracted from the derivatized TBIwaveform. The derivatized ICG waveform features consecutive pulses, eachcharacterized by three points: a ‘B’ point on the pulse's upswingindicating opening of the aortic valve; an X point on the pulse's nadirindicating closing of the aortic valve; and a ‘C’ point on its maximumvalue indicating the maximum slope of the ΔZ(t) pulse's upswing, whichis equivalent to (dZ/dt)_(max). LVET is typically calculated from thetime differential between the B and X points. However, due to the subtlenature of these fiducial markers, even low levels of noise in thewaveforms can make them difficult to determine. Ultimately such noiseadds errors to the calculated LVET and resulting SV.

The analysis described above was used in a formal clinical study to testaccuracy of determining SV using a technique similar to TBI and Eq. 3above, compared to SV determined using MRI. Correlation and Bland-Altmanplots are shown, respectively, in the right and left-hand sides of FIG.16. The shaded gray area in the plots indicates the inherent errorsassociated with conventional Doppler/ultrasound measurements, which areabout +/−20%. In total 26 subjects (14M, 12W) with ages ranging from21-80 were measured for this study, and correlations for all of thesesubjects fell within the error of the MRI measurements.

FIGS. 17 and 18 show data collected from a sensor operating an impedancemeasurement, similar to that described above, on patients undergoing anexperimental protocol called ‘lower body negative pressure’ (LBNP).During LBNP, a patient is inserted into a vacuum chamber up to theirwaist. The vacuum chamber then applies a gradually increasing vacuum tothe patient's lower extremities, thereby essentially sucking blood andother fluids from the patient's thoracic cavity into the legs and waist.In this way, LBNP works in an opposite manner to CHF, i.e. it graduallyreduces thoracic fluids, as opposed to increasing them. Traditionally,LBNP is used to simulate hemorrhage in patients because it essentiallyremoves blood from the body's major organs. Typically during hemorrhageSV is decreased. Such a reduction can also occur during CHF, and thusLBNP is an ideal experimental technique for inducing changes in twoproperties—thoracic fluid level and SV—that also undergo atime-dependent change during CHF.

FIG. 17 shows pooled results from 39 subjects undergoing a gradualincrease in LBNP from 0 mmHg (i.e. no change from ambient) to a vacuumof 60 mmHg (corresponding to a loss of blood of about 2 L). The datashown in this figure are averaged over all 39 subjects, and impedancewaveforms similar to those described above were measured from thethoracic cavity and analyzed to determine SV and thoracic fluid level.As shown in the top portion of the figure, the change in baselineimpedance correlates in a linear manner with the LBNP level, with theagreement between these parameters (Pearson's correlation coefficientr²=0.9998) being extremely high. Here, vacuum applied during LBNPgradually removes conductive fluids from the thoracic cavity, thusdecreasing conductivity and increasing baseline impedance. Similarly,the relationship between LBNP level and SV shown in the bottom half ofthe plot is also linear, with the slope going in the opposite directionas that for the impedance/LBNP correlation. In this case increasing LBNPremoves blood from the patient's thoracic cavity, thus reducing theireffective blood volume (called ‘pre-load’) and essentially simulatinghemorrhage. During hemorrhage, the body is trained to reduce blood flowby decreasing the amount of blood pumped by the heart (the SV) topreserve perfusion of the internal organs. Thus, it is expected thatincreasing LBNP will systematically decrease SV, which is exactly whatis shown in the lower half of FIG. 17. The correlation for thisrelationship is also quite high, with r²=0.99531.

In conclusion, the results shown in FIG. 17 indicate that two parametersthat change with the onset of CHF—thoracic fluid level and SV—can beaccurately measured with an impedance-based technique, such as thatdeployed with the sensor described herein.

The data shown in FIG. 17 are averaged over all 39 subjects, while theindividual correlation coefficient for each subject for theabove-described measurements are shown in FIG. 18. As is clear fromthese data, 36 out of 39 subjects show a correlation between LBNP level(representing a proxy for fluid level, as described above) and baselineimpedance characterized by r>0.98, which is extremely high. Similarly,36 out of 39 subjects show a correlation between LBNP level and SVcharacterized by r>0.9. Both of these plots indicate that the parametersmeasured by impedance measurements show promise for being an accuratephysiological monitor.

FIG. 19 shows how the above-described sensor integrates into a web-basedsystem for treating a patient with a process called electrophysiology(EP). EP is used, for example, to treat patients suffering fromarrhythmias whom may not be candidates for an implanted device, such asa pacemaker. In embodiments, the EP system shown in the figure issimilar to that described in the co-pending patent application entitledINTERNET-BASED SYSTEM FOR COLLECTING AND ANALYZING DATA BEFORE, DURING,AND AFTER A CARDIOVASCULAR PROCEDURE (U.S.S.N 61/711,096; filed Oct. 8,2012), the contents of which are incorporated herein by reference.

As shown in the figure, a patient 310 is treated with an EP System 364,such as the Bard LabLink™ Data Interface, that synchronizes andintegrates 3D mapping systems (e.g. the Carto® 3 System) with EPRecording Systems (e.g. the LabSystem™ PRO EP Recording System). The EPSystem 364 allows selection of stimulation channels from either therecording or mapping system, and merges patient demographics, 3D imagesnapshots and cardiovascular event data, e.g. waveforms measured withinternal electrodes, refractory periods, and ablation information.During an EP procedure, the EP System 364 outputs an XML file thatincludes these data, encoded as either numerical values or waveforms.The XML file passes to a Database 368, where an XML parsing enginedecodes it before the data elements are stored in specific fields, asdescribed in more detail below.

An EP Module 366 also provides data for the Database 368. The EP Module366 is preferably a system that collects information during the EPprocedure, such as data describing: i) patient demographics; ii) vitalsigns; iii) supplies used during the EP procedure; iv) billinginformation; and v) clinician information.

During the EP procedure, data from the EP System 364 and EP Module 366flow from the Database 368 into the patient's Electronic Health Record370, which is usually associated with an enterprise-level,medical-records software system deployed at the hospital, such as thatprovided by Epic or Cerner. Data from the Electronic Health Record 370can be further processed by a Cloud-Based Data Analytics System 372,which is similar to that described in the above-mentioned patentapplication, the contents of which have been previously incorporatedherein by reference. As described in this patent application, theCloud-Based Data Analytics System 372 processes physiological,procedural, and operational data collected before, during, and after theEP procedure to generate custom reports and perform numerical studies.The above-referenced patent application includes several examples of howthe Cloud-Based Data Analytics System 372 can process physiological datato evaluate the patient and the EP procedure overall. Additionally, aCardiac Mapping System 374 processes CO, SV, HR, and ECG data measuredby a Body-Worn Sensor 60 to generate 3D images of the patient's heart. AMobile Application 362, similar to that shown in FIG. 10, also receivesdata wirelessly from the Body-Worn Sensor 360, described in detailbelow, thereby allowing a clinician to remotely monitor the patient 310.

Systems similar to that described above can also be used for othercardiac procedures conducted in other areas of the hospital, such as thecatheterization laboratory, medical clinic, or vascular analysislaboratory. In these applications, data other than HR and ECG waveformsmay be analyzed using techniques similar to those described above. Dataused in these examples includes medical images (such as those measuredusing MRI or Doppler/ultrasound), all vital signs, hemodynamicproperties such as cardiac output and stroke volume, tissue perfusion,pH, hematocrit, and parameters determined with laboratory studies.

In other embodiments, signals from the wireless transceiver within thesensor can be analyzed (e.g. triangulated) to determine the patient'slocation. In this case, a computer operating at a central monitoringstation, such as that used at a hospital, can perform triangulation.Alternative, the patient's cellular telephone can be used for thispurpose. In still other embodiments, the sensor can include a moreconventional location system, such as a global positioning system (GPS).In this case the GPS and its associated antenna are typically includedon a rigid circuit board that connects to the data-processing systemwithin the sensor.

In still other embodiments, the necklace-shaped sensor can be augmentedto include other physiological sensors, such as a pulse oximeter orblood pressure monitor. For example, the pulse oximetry circuit can beincluded on a rigid circuit board within the necklace, and then canconnect to an ear-worn oximetry sensor. The geometry of the sensordescribed herein, and its proximity to the patient's ear, makes thismeasurement possible. For blood pressure, a parameter called pulsetransit time, which is measured between a fiducial point on the ECGwaveform (e.g. the QRS complex) and a fiducial point (e.g. an onset) ofa TBI pulse (such as the C point shown in FIG. 15), correlates inverselyto blood pressure. Thus measuring this parameter and calibrating it witha conventional measurement of blood pressure, such as that done with anoscillometric cuff, can yield a continuous, non-invasive measurement ofblood pressure.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A sensor for measuring fluid in a patient'schest, comprising: a flexible member configured to drape around thepatient's neck and connect at its ends to form a continuous loop shape;at least two electrodes, each connected to the flexible member andconfigured to measure electrical signals from the patient; animpedance-measuring system, comprised by the flexible member and inelectrical contact with the at least two electrodes, theimpedance-measuring system configured to receive the electrical signalsfrom the at least two electrodes and process them to determine animpedance value; and, a data-processing system, comprised by theflexible member and in electrical contact with the impedance-measuringsystem, the data-processing system operating a computer algorithmconfigured to process the impedance value to measure fluid in thepatient's chest.
 2. The sensor of claim 1, comprising at least fourelectrodes.
 3. The sensor of claim 2, comprising a firstcurrent-injecting electrode, a second current-injecting electrode, afirst voltage-measuring electrode, and a second voltage-measuringelectrode.
 4. The sensor of claim 3, wherein the flexible membercomprises a first segment comprising the first current-injectingelectrode and the first voltage-measuring electrode, the first segmentconfigured to contact a first portion of the patient's chest, and asecond segment comprising the second current-injecting electrode and thesecond voltage-measuring electrode, the second segment configured tocontact a second, opposing segment of the patient's chest.
 5. The sensorof claim 4, wherein the first segment comprises a first rigid member,and the second segment comprises a second rigid member, and both thefirst and second rigid members comprise connectors configured to connectto the electrodes.
 6. The sensor of claim 5, wherein the flexible membercomprises a flexible, current-conducting member that connects theelectrodes comprised by the first and second rigid members to theimpedance-measuring system.
 7. The sensor of claim 6, wherein theimpedance-measuring system is configured to receive electrical signalsthat are measured by the first and second voltage-measuring electrodesand pass through the flexible, current-conducting member.
 8. The sensorof claim 7, wherein the impedance-measuring system comprises adifferential amplifier.
 9. The sensor of claim 8, wherein thedifferential amplifier is configured to measure a time-dependent voltageindicating the product of electrical impedance in the patient's chestand current injected by the current-injecting electrodes.
 10. The sensorof claim 9, wherein the differential amplifier is configured to measurea first time-dependent voltage indicating how fluid levels andrespiration affect electrical impedance in the patient's chest.
 11. Thesensor of claim 10, wherein the data-processing system is configured tooperate a first computer algorithm configured to process the firsttime-dependent voltage to estimate the fluid levels in the patient'schest.
 12. The sensor of claim 10, wherein the data-processing system isfurther configured to operate a second computer algorithm configured toprocess the first time-dependent voltage to estimate the patient'srespiration rate.
 13. The sensor of claim 9, wherein the differentialamplifier is configured to measure a second time-dependent voltageindicating how heartbeat-induced blood flow affects electrical impedancein the patient's chest.
 14. The sensor of claim 13, wherein thedata-processing system is further configured to operate a first computeralgorithm configured to process the second time-dependent voltage toestimate the patient's stroke volume.
 15. The sensor of claim 13,wherein the data-processing system is further configured to operate asecond computer algorithm configured to process the secondtime-dependent voltage to estimate the patient's cardiac output.
 16. Thesensor of claim 13, wherein the data-processing system is furtherconfigured to operate a third computer algorithm configured to processthe second time-dependent voltage to estimate the patient's heart rate.17. The sensor of claim 1, wherein the flexible member forming thecontinuous loop shape has a circumference of at least 40 cm.
 18. Thesensor of claim 17, wherein the flexible member forming the continuousloop shape has a width of less than 3 cm.
 19. A sensor for measuringimpedance in a patient's chest, comprising: a flexible member configuredto drape around the patient's neck and connect at its ends to form acontinuous loop shape; a collection of at least two electrodes, eachconnected to the flexible member and configured to measure electricalsignals from the patient; and an impedance-measuring system, comprisedby the flexible member and in electrical contact with the at least twoelectrodes, the impedance-measuring system configured to receive theelectrical signals from the at least two electrodes and process them todetermine an impedance value.
 20. A sensor for measuring impedance in apatient's chest, comprising: a flexible member configured to drapearound the patient's neck and connect at its ends to form a continuousloop shape; a collection of four electrodes, each comprised by theflexible member, with two electrodes configured to inject current intothe patient's chest, and another two electrodes configured to measureelectrical signals from the patient's chest; an impedance-measuringsystem, comprised by the flexible member and in electrical contact withthe two electrodes configured to measure electrical signals from thepatient's chest, the impedance-measuring system configured to receivethe electrical signals from the two electrodes and process them todetermine an impedance value.